Why combining datasets creates complete pictures in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to merge two dataframes on the 'id' column.

Data Analysis Python

merged_df = df1.merge(df2, on=[1])

Drag options to blanks, or click blank then click option'

A'name'

B'id'

C'age'

D'score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column that doesn't exist in both dataframes.

Forgetting to put quotes around the column name.

We merge datasets on the common column 'id' to combine matching rows.

2fill in blank

medium

Complete the code to concatenate two dataframes vertically.

Data Analysis Python

combined_df = pd.concat([df1, df2], axis=[1])

Drag options to blanks, or click blank then click option'

A0

B-1

C1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 which stacks columns instead of rows.

Using an invalid axis number.

axis=0 stacks dataframes vertically, adding rows.

3fill in blank

hard

Fix the error in the code to merge dataframes with an inner join.

Data Analysis Python

result = df1.merge(df2, how=[1], on='user_id')

Drag options to blanks, or click blank then click option'

A'outer'

B'left'

C'inner'

D'right'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'outer' which keeps all rows, not just matching ones.

Using 'left' or 'right' which keep all rows from one dataframe only.

An inner join keeps only rows with matching keys in both dataframes.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using '<' instead of '>' in the condition.

We map each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Data Analysis Python

result = [1]: [2] for k, v in data.items() if v [3] 0}

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' for keys.

Using '<' instead of '>' in the condition.

Using keys as values or vice versa.

Keys are uppercase, values are original, filtered by values greater than zero.

Practice

1. Why do we combine datasets in data analysis?

easy

A. To make data smaller by removing columns

B. To delete unnecessary data permanently

C. To get a more complete view by joining related information

D. To create random data for testing

Solution

1. Step 1: Understand the purpose of combining datasets

   Combining datasets helps bring together related pieces of information from different sources.

2. Step 2: Recognize the benefit of combining

   This creates a fuller, more complete picture for analysis.

3. Final Answer:

   To get a more complete view by joining related information -> Option C

4. Quick Check:

   Combining datasets = fuller picture [OK]

Hint: Combining means joining info to see the whole story [OK]

Common Mistakes:

• Thinking combining deletes data
• Confusing combining with filtering
• Believing combining creates random data

2. Which of the following is the correct syntax to merge two pandas DataFrames df1 and df2 on a common column 'id'?

easy

A. pd.merge(df1, df2, on='id')

B. df1.merge(df2, by='id')

C. merge(df1, df2, key='id')

D. pd.join(df1, df2, on='id')

Solution

1. Step 1: Recall pandas merge syntax

   The correct function is pd.merge() with parameters for the two DataFrames and the column name using on='id'.

2. Step 2: Check each option

   pd.merge(df1, df2, on='id') uses correct function and parameter names. Others use incorrect method names or parameters.

3. Final Answer:

   pd.merge(df1, df2, on='id') -> Option A

4. Quick Check:

   Correct merge syntax = pd.merge(df1, df2, on='id') [OK]

Hint: Use pd.merge() with on='column' to combine DataFrames [OK]

Common Mistakes:

• Using wrong method like df1.merge with 'by' instead of 'on'
• Using pd.join which does not exist
• Passing wrong parameter names like 'key'

3. Given these two DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 3], 'value1': ['A', 'B', 'C']})
df2 = pd.DataFrame({'id': [2, 3, 4], 'value2': ['X', 'Y', 'Z']})
result = pd.merge(df1, df2, on='id', how='inner')

What is the content of result?

medium

A. {'id': [2, 3], 'value1': ['B', 'C'], 'value2': ['X', 'Y']}

B. {'id': [1, 2, 3, 4], 'value1': ['A', 'B', 'C', NaN], 'value2': [NaN, 'X', 'Y', 'Z']}

C. {'id': [1, 2, 3], 'value1': ['A', 'B', 'C'], 'value2': [NaN, 'X', 'Y']}

D. {'id': [2, 3, 4], 'value1': ['B', 'C', NaN], 'value2': ['X', 'Y', 'Z']}

Solution

1. Step 1: Understand inner join behavior

   Inner join keeps only rows with matching 'id' in both DataFrames. Here, ids 2 and 3 appear in both.

2. Step 2: Match rows and combine columns

   Rows with id 2 and 3 combine values from both DataFrames. Others are dropped.

3. Final Answer:

   {'id': [2, 3], 'value1': ['B', 'C'], 'value2': ['X', 'Y']} -> Option A

4. Quick Check:

   Inner join = only matching ids kept [OK]

Hint: Inner join keeps only rows with matching keys in both sets [OK]

Common Mistakes:

• Confusing inner join with outer join
• Expecting all ids to appear
• Mixing up which columns get NaN

4. You try to merge two DataFrames df1 and df2 on column 'key' using pd.merge(df1, df2, on='key') but get a KeyError: 'key'. What is the likely cause?

medium

A. You must use how='outer' to avoid KeyError

B. The column 'key' does not exist in one or both DataFrames

C. You forgot to import pandas as pd

D. The DataFrames have different numbers of rows

Solution

1. Step 1: Understand KeyError in merge context

   A KeyError for the merge column means that column is missing in one or both DataFrames.

2. Step 2: Check other options

   Using different join types or import issues do not cause KeyError on column name. Row counts do not matter for this error.

3. Final Answer:

   The column 'key' does not exist in one or both DataFrames -> Option B

4. Quick Check:

   KeyError means missing column [OK]

Hint: KeyError on merge means missing column in data [OK]

Common Mistakes:

• Thinking join type fixes KeyError
• Assuming import error causes KeyError
• Believing row count mismatch causes KeyError

5. You have two datasets:

sales = pd.DataFrame({'store_id': [1, 2, 3], 'sales': [100, 200, 300]})
stores = pd.DataFrame({'store_id': [2, 3, 4], 'location': ['NY', 'LA', 'SF']})

You want a combined table showing all stores with their sales if available, and location if available. Which merge command gives this complete picture?

hard

A. pd.merge(sales, stores, on='store_id', how='inner')

B. pd.merge(sales, stores, on='store_id', how='left')

C. pd.merge(sales, stores, on='store_id', how='right')

D. pd.merge(sales, stores, on='store_id', how='outer')

Solution

1. Step 1: Understand join types and goal

   You want all stores from both datasets, showing sales and location where available.

2. Step 2: Choose join type that keeps all rows

   Outer join keeps all rows from both DataFrames, filling missing values with NaN.

3. Step 3: Verify options

   Inner join keeps only matching stores. Left or right join keep only one side's stores. Outer join keeps all.

4. Final Answer:

   pd.merge(sales, stores, on='store_id', how='outer') -> Option D

5. Quick Check:

   Outer join = all data combined [OK]

Hint: Use outer join to keep all rows from both datasets [OK]

Common Mistakes:

• Using inner join and losing unmatched rows
• Confusing left/right join with full outer join
• Expecting missing data to be dropped automatically